TensorFlow GPU Recognition Issue on Jetson Orin Nano with JetPack 6.0

Issue Overview

Users are experiencing difficulties with TensorFlow not recognizing or utilizing the GPU on the Nvidia Jetson Orin Nano development board running JetPack 6.0. The primary symptom is that TensorFlow is not working on the GPU, despite the expectation that CUDA tools should be pre-installed with JetPack 6.0. This issue impacts the ability to leverage GPU acceleration for TensorFlow-based machine learning tasks, potentially affecting performance and functionality of AI applications on the Jetson Orin Nano platform.

Possible Causes

1. Incomplete CUDA installation: Although JetPack 6.0 is expected to include CUDA tools, there might be missing components or an incomplete installation.

2. TensorFlow configuration: The TensorFlow installation may not be properly configured to detect and use the available GPU.

3. Incompatible TensorFlow version: The installed TensorFlow version might not be compatible with the specific CUDA version or Jetson Orin Nano hardware.

4. Driver issues: There could be problems with GPU drivers or their integration with TensorFlow.

5. Environmental variables: Incorrect or missing environment variables could prevent TensorFlow from recognizing the GPU.

Troubleshooting Steps, Solutions & Fixes

1. Use NVIDIA’s prebuilt TensorFlow package:
   NVIDIA recommends using their prebuilt TensorFlow package, which has CUDA support enabled. Follow the installation guide provided in the NVIDIA documentation.

   To install the prebuilt package, you can use the following command:

   sudo apt-get install python3-tensorflow
   

2. Verify CUDA installation:
   Check if CUDA is properly installed and recognized by the system:

   nvcc --version
   

   If CUDA is not found, you may need to install or reinstall the CUDA toolkit.

3. Check TensorFlow GPU support:
   Run a Python script to verify if TensorFlow can see the GPU:

   import tensorflow as tf
   print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
   

4. Update environment variables:
   Ensure that the necessary environment variables are set correctly:

   export PATH=/usr/local/cuda/bin:$PATH
   export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
   

   Add these lines to your ~/.bashrc file for persistence.

5. Reinstall TensorFlow:
   If issues persist, try reinstalling TensorFlow:

   pip3 uninstall tensorflow
   pip3 install tensorflow
   

6. Check JetPack version compatibility:
   Ensure that the installed TensorFlow version is compatible with JetPack 6.0. Refer to NVIDIA’s documentation for version compatibility information.

7. Update JetPack:
   If you’re not on the latest version of JetPack 6.0, consider updating:

   sudo apt update
   sudo apt upgrade
   

8. Consult Jetson community forums:
   If the issue persists, consider posting a detailed description of your problem in the Jetson & Embedded Systems category of the NVIDIA Developer forums for more specific assistance.

Remember to reboot your Jetson Orin Nano after making significant changes to ensure all updates take effect. If none of these solutions resolve the issue, it may be necessary to file a bug report with NVIDIA or seek further assistance from their technical support team.